Data selection is reframed as dynamic programming over an MDP, existing data values are shown to be myopic linear approximations, and a bipartite coverage surrogate is proposed, but its exact optimality guarantee is unsound.
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Unifying and Optimizing Data Values for Selection via Sequential Decision-Making
Data selection is reframed as dynamic programming over an MDP, existing data values are shown to be myopic linear approximations, and a bipartite coverage surrogate is proposed, but its exact optimality guarantee is unsound.